Second, minimal rules (i.e., rules that cannot be obtained from other extracted rules) typically consist of a few lexical items only and are thus not the most suitable to translate idiomatic expressions and other fixed phrases.

to an SCFG each placeholder can have several such links.


The phrase translation probability feature selects phrases that occurs more frequently in the training corpus, which sometimes is long with a lower translation probability, as in translating named entities or idioms; sometimes is short but with a high translation probability, as in translating verbs or pronouns.

we take a harsh step and constrain the nodes in hidden layer to have a in-degree of two, which means each hidden node only accepts inputs from two input nodes.


They can be largely divided into two groups.

with 100k tokens of data, the baseline achieves 1.2% accuracy, and DMRE improves it to 2.4%.


We leave automatic learning of α e for future work.

we often also see translation clusters, where translations of groups of words are close to each other.


However, the executions of a given sentence can have widely different lengths, which could make incremental inexact decoding such as beam search difficult due to the need to prune over partial hypotheses that have different numbers of emitted words.

this internal RNN has a separate clock and a separate state vector.


Given the log in Figure 2 as input, players 5 (detective) and 7 (killer) are clustered into one group when DAPC is applied since they don't have conflicting views on the identities of other players.

positive terms include "citizen", "good person", "good person certified by the detectives" and "detective".


People may form groups for purpose of deception.

in daily life, millions of fraud cases involve detailed conversations between deceivers and victims.


Generated summaries are both informative and precise.

setting d to 0.80 in our experiments provided the best content selection results.


Our ILPbased abstractive summarization system fuses and selects content from multiple sentences, thereby aggregating information successfully from multiple sources.

dataset Construction: As mentioned earlier in Section 3.1, we crawl Wikipedia articles by traversing the category graph.


The core components of KRISP are string-kernel classifiers P (r, i..j|E) denoting the probability that a production r in the AST covers the span of words i..j in the sentence E. Here, i < j are positions in the sentence indicating the span of tokens most relevant to this production.

we used a phrasal decoder with performance similar to Moses.


We gather the k-best translations, retaining the first such output that can be successfully converted into a well-formed program according to the formal grammar.

kRISP was shown to be much more robust to noisy training data than previous approaches to semantic parsing (kate and Mooney, 2006).


After exploring multiple word alignment approaches, we found that an unsupervised feature-rich method (Berg-Kirkpatrick et al., 2010) worked best, leveraging features of string similarity between the description and the code.

our levels of agreement are likely to be greater than suggested by measures in the table.


Note that the augmenting article segments were removed from the training/test sets.

the generation of these questions is restricted to the first sentence of the paragraph.


This algorithm enumerates all configurations such that each character pair has a common pattern type or is matched 1-for-1 with a character pair with common variable types, which is exactly the definition of a rewriting rule in TESRK.

the pair "Former French president General Charles de Gaulle died in November.


(2014) apply simplification rules to image captions, showing that the resulting hierarchy of mappings between natural language expressions and images can be used for entailment tasks.

for each predicate v, we compute the interpolation weights based on its prominence in the respective corpus, as follows: where rel is the relevance function of model i for verb v, computed as its relative frequency in the respective corpus: .


We extract linguistic features for our model from the BNC.

clustering with linguistic features: We first cluster the 2,000 most frequent nouns in the BNc, using their grammatical relations as features.


This means that certain classes of entities are more likely to fill the predicate's argument slot than others.

the roles that individual words play are still discernible from their visual context, as manifested by the other words in a given set.


We therefore define the reliability of the DICT action as 0.67.

the mapping from tokens of a sentence to AMR nodes is not one-to-one.


We consider two metrics, IED and END, which measure accuracy based on the action sequence and environment, respectively.

while this generalization gives us a mapping ξ, we create a lexical entry i = [ν ⇒ (λ v.S, ∅)] without it, where S is the parameterized atom.


Note that (i) any feature of the space is basically a pair of substructures; and (ii) there is currently no efficient (implicit) formulation for computing such kernel.

the most intuitive applies K between all member combinations and sum all contributions: (iv) It is also possible to combine pairs of corresponding kernels with the product: (v) An alternative kernel selects only the best between the two products above: .


This establishes a range of values, instead of a single point, in which SLs can be said to agree.

in our experimentation we are able to quickly uncover numerous intra-and inter-lexicon inconsistencies in all of the input SLs tested and to suggest lexicon entries for a linguist to focus on in "debugging" the lexicon.


But in general replacing the squared 2 norm with an 1 penalty (over all weights) hampers classification performance (indeed, as we later report, this strategy performs very poorly here).

2013proposed a method that exploits apparently contrasting sentiment in the same utterance to detect irony.


Figure 4 gives an example of our dependence factor graph (DFG) when two instances, i.e., sentence-1 and sentence-2 are involved.

in the future work, we would like to explore better ways of modeling the label and context dependence and apply our DFG approach in more applications, e.g.


Generating attitude feature set Once the sentence level is done, the T rue value is assigned to the relevantAttExpr(usrSentence) variable in two steps.

the third class, called other, deals with all entities and processes which are neither the agent or the user.


On the one hand, we obtain low dimensional embeddings fitting the complexity of the target task.

first, the input layer is factorized into two components, the embeddings attained in unsupervised form, E, and the projection matrix S. Second, the size of the sub-space, in which the embeddings are projected, is much smaller than that of the original embeddings with typical reductions above one order of magnitude.


Furthermore, rare words will receive very few updates, and thus their embeddings will be poorly adapted for the intended task.

this strategy faces an additional challenge in noisy domains, such as social media.


• We show that the features derived from crowdsourced transcriptions perform as well as crowd grades in predicting expert grades.

this clearly shows the value of getting accurate transcription from workers towards better features and model.


First, the 1-best transcriptions from multiple systems are aligned by means of dynamic programming to build a single, minimal word transition network.

"•" indicates that the WER score is not significantly different from the system-based ROVER oracle (a positive result); 3. "


RR1+WordBased slightly improves over RR1+Basic, indicating the possible usefulness of this new set of features.

schwenk and Gauvain (2000) exploit a secondary language model to rescore the final n-best hypotheses generated by ROVER.


its bestknown hardware products are the mac line of computers , the ipod media player , the iphone smartphone , and the ipad tablet computer .

paragraphs with larger than 4 percent of unknown words are discarded.


They often are pregrouped as words-with-spaces in many parsing architectures (Sag et al., 2002).

we propose to jointly parse and tokenize MwEs, transforming segmentation decisions into linking decisions.


We also performed experiments on the SMS domain, using data annotated by the LDC for the DARPA BOLT Program.

comparing with their method, our model is much simpler.


We found increasing hiddenSize is helpful for parsing performance.

the performance trend for the Mix-train strategy is different from that of Chinese.


The choice of these hyperparameters may affect the final performance.

rather than performing full inference, their model can only score parse candidates generated from another parser.


Removing the confounding presence of punctuation from parsing and treating attachment as a global post-process may help to alleviate these issues.

these cascaded errors appear to be caused by the incorrect root.


where α ∈ [0, 1] is a hyperparameter; s t (x i , y, Θ) and s b (x i , y) are the scores given by RCNN and the base parser respectively.

multiple recursive combination functions have been explored, from linear transformation matrices to tensor products (Socher et al., 2013c).


For a given training instance (x i , y i ), we use the max-margin criterion to train our model.

the ability of these models is restricted by the design of features.


The use of neural networks for transition-based shift-reduce parsing was first presented by Mayberry and Miikkulainen (1999) in which the stack representation was treated as a hidden state of an RNN.

(2013), which adopts the shift-reduce parsing of Sagae and Lavie (2005) and Zhang and Clark (2009).


A popular parsing algorithm is a cubic time chartbased dynamic programming algorithm that uses probabilistic context-free grammars (PCFGs).

experimental results obtained using the Wall Street Journal corpus of the english Penn Treebank achieved a labeled F1 score of 90.68%, and the result for the Penn Chinese Treebank was 84.33%.


For testing, we found that taking the average of the parameters over period 1 T t=0 θ t under training iterations T was very effective as demonstrated by Hashimoto et al.

the parsing speed was influenced by only the hidden state size m. It is clear that the enlarged beam size improves per- 16 http://sourceforge.net/projects/zpar/ parser test ZPar (Zhu et al., 2013) 83.2 Berkeley (Petrov and Klein, 2007) 83.3 Joint (Wang and Xue, 2014) 84.9 This work: TNCP 84.3  formance by trading off run time in most cases.


One common decoding technique is to implement a dynamic program, thus avoiding the explicit enumeration of all analyses for a given timestamp (Huang, 2008).

all other leaves are marked with a↓ and are called substitution nodes.


Incremental parsing, however, is considerably harder than full-sentence parsing: when processing the n-th word in a sentence, a n , the parser only has access to the left context (words a 1 .

to all the joint approaches, we perform both parsing and semantic role labeling strictly incrementally, without having access to the whole sentence, outputting prefix trees and iSRL triples for every sentence prefix.


We use the same sub network as Chen and Manning (2014) to calculate o(x, y i , k, a k ) (Equation 5).

the greedy neural parser of Chen and Manning (2014) gives higher accuracies compared to the greedy linear MaltParser (Nivre and Scholz, 2004), but lags behind state-of-the-art linear systems with sparse features (Zhang and Nivre, 2011), which adopt global learning and beam search decoding (Zhang and Nivre, 2012).


Compounding the problem, the accuracy of statistical parsers degrades as the domain shifts away from the supervised training corpora (Gildea, 2001;Bacchiani et al., 2006;McClosky et al., 2006b;Surdeanu et al., 2008).

building treebanks is expensive and timeconsuming for humans.


We re-implement the PROJECTED cluster approach in Täckström et al.

our combined contributions achieve an average relative error reduction of 10.9% in labeled attachment score as compared with the delexicalized parser, trained on English universal treebank and transferred to three other languages.


We point out that these numbers are an over-estimation since many of the markers occur in syntactic roles as opposed to discourse in the sentences (ex.

we find that out of the 103 annotated sentence pairs, 67 are next to each other in the passage while 27 are at a distance of two and 9 pairs are at a distance of three or more.


it has been well documented that identifying discourse relations without explicit markers is significantly harder than with markers (Pitler et al., 2008;Lin et al., 2009;Park and Cardie, 2012).


The discourse information is typically computed using discourse parsers based on frameworks like RST (Feng and Hirst, 2014) or PDTB (Lin et al., 2014), trained using supervised data.

for each annotated pair of sentences, we calculate the relation distribution and compute the relation recall at various thresholds of the ranking by probability.


We add in a hidden variable r ∈ R to represent the relation type.

we are interested in extracting discourse relations with minimal additional annotation, relying primarily on the available question-answer pairs.


as the frames evoked in two sentences are both arriving, we link the antecedent of Goal in the second sentence to [Tiananmen Square/US €2|], which is the content of Goal in the first sentence.


The set of FEs is further split into core FEs and non-core FEs.

resolving DNIs is modeled in the same way as labeling overt arguments, with the search space being extended to nouns, pronouns, and noun phrases from the previous three sentences.


Finally, NEG (negated) marks negatively quantified entities that refer to the empty set of the kind mentioned.

the two-step approach labels more instances as NON-GEN gen but does so in a less precise way.


We only preserve word types that appear more than 100 times and replace all others with a special symbol, resulting in a vocabulary of size around 188k.

another novelty of our work is considering a lowrank approximation of a linear regressor that predicts the context from words.


(2014) which uses CCA for learning the class of HMMs underlying Brown clustering.

we would like to estimate O and use its rows for representing word types.


This is confirmed by a significant (p < 0.001) result from a logistic regression of correctness against cosine value.

modifying the original model to retain the sequential information in the input is relatively straightforward.


Each node in the tree then requires both a bag vector, n b , to handle the bag context, and 2k sequence vectors, n s i , to handle the sequence context vectors, with probabilities given by: A second source of information which might be used to differentiate semantic from syntactic representations is morphology.

the semantic task correlates predicted semantic similarities with the noun-verb similarity ratings gathered by Mitchell (2013), and the remaining task clusters the syntactic representations and evaluates these clusters in relation to the POS classes found in the Penn treebank.


Each classifier   is trained using the annotated Wikipedia categories.

we compare performance to a number of ablated versions of the full system, where we have removed the open-constant ontology matching operators (NoOpenSchema), the PMI features (NoPMI), or the type checking features (No-Typing).


The intuition behind is that even if a query is not completely correct, it is still preferred than some other totally incorrect queries.

the graph can be straightforwardly translated to a logical form query that is directly executable.


Generating directly from the grammar in Table 2 would result in many uninterpretable canonical utterances.

we not only use paraphrasing in the semantic parser, but also for data collection.


The derivatives with respect to the hidden activation of position i at the last time step T are computed as follow: where v i represents the weights of hidden-output connection and the activation of the output layer y is used to estimate probability of the tweet bearing a particular polarity.

this is due to the fact that tuned embeddings capture the sentiment information of text by distinguishing words with opposite sentiment polarities and providing more flexibility for composing.


Polarities of words are identified based on a widely-used sentiment lexicon (Hu and Liu, 2004).

bag-of-words representations are not powerful enough.


Accuracy(%) MNB+unigram+bigram 32.98 RNN-TLT 52.00 LSTM-TLT 64.85 Table 4: Accuracy on generated negation phrases test set.

words like good and bad have very similar representations (Socher et al., 2011).


The simple ap-proach is to define a sentiment score of a given aspect by the weighted sum of opinion scores of all words in the sentence, where the weight is defined by the distance from the aspect (Liu and Zhang, 2012;Pang and Lee, 2008).

they concluded that their model does not successfully predict stock returns.


This is the first research that shows good prediction results on evaluation of many stocks using a test set consisting of many transaction dates.

how to use opinions in social media for stock price predictions is still an open problem.


If we use different composition functions for children with different tags like TG-RNN, the number of tag pairs will amount to as many as k×k, which makes the models infeasible due to too many parameters.

vectors of interior nodes are computed recursively by composition of child nodes' vectors.


the universal treebanks that our approach leads to competitive results, while being computationaly very efficient.

computing an optimal solution of this problem is obtained by finding a minimum weight spanning tree.


While unsupervised methods have been considered an attractive option for low-resource parsing, they typically rely on large quantities of clean, raw sentences.

the complete internal structure of the phrase the equity division remains unspecified, other than division being marked as the head (via an asterisk).


The most interesting component of our analysis is the long tail of constructions that must be captured in order to produce semantically appropriate representations.

in the as getting the wrong head leads to the company laughing or other semantically nonsensical analyses.


The first two values depend on the current model, so the saved values must be cleared between iterations of training.

furthermore, the costs provide our system a measure of the severity of a mistake, which we argue is very beneficial for the coreference task.


The model is entity-centric in that it builds up clusters of mentions representing entities and merges clusters if it predicts they are representing the same one.

for each pair, we make a binary decision on whether or not the clusters containing these pairs should be merged.


The primary weakness of this approach is that it only relies on local information to make decisions, so it cannot consolidate information at the entity level.

our model stacking approach further distinguishes this work by providing a new way of defining cluster-level features.


Furthermore, rather than considering all pairs of clusters as candidate merges, we use the scores of the pairwise models to reduce the search space, first by providing an ordering over which merges are considered and secondly by discarding merges that are not likely to be good.

our model also achieves the highest CEAF φ 4 F 1 and second highest MUC F 1 scores among the other systems.


That is, we minimize KL(Q||p w ) := min q∈Q KL(q p w ).

note also that, while the vanilla PR method achieves higher recall than the two other baselines, it is still almost 5% below the system trained with soft-max margin PR.


We split all MWEs into individual tokens (for consistency with the other corpora).

the inclusion of lexicalized features by  yields nearly state-of-the-art performance with surface information only.


To analyze intra-Republican polarization, we first compute, for each issue k, the span of ideal points the frames associated with k, i.e., the difference between the maximum and the minimum ideal points for frames under that issue.

in this hierarchical structure, first-level nodes map to agenda issues, which we treat as non-polarized, and second-level nodes map to issue-specific frames, which we assume polarize on the issue-specific dimension.


model, HIPTM, also outperform TF and TF-IDF significantly, but only slightly better than VOTE.

(2013) and samplê where ) is the token count vector from the bill text assigned to each issue.


In this section, HIPTM reveals how issue-specific ideal points of the two groups of Republican representatives differ.

it still quite strongly outperforms the two text-based baselines, showing that jointly modeling the voting behavior improves the text-based elements of the model.


found that this modification allowed TNG to outperform LDACOL on a standard information retrieval task.

both counts exclude the current c 2 , and also exclude c 1 if z 1 = z 2 and c 1 = c 2 .


crucially, D a only depends on the algebra and a itself, and not on G or h, because D contains all terms that evaluate to a and not just those that are licensed by the grammar.


This is familiar from string parsing, where a CKY parse chart spells out all the ways in which larger substrings can be decomposed into smaller parts by concatenation.

if we see SG 1 and SG 2 as subgraphs of SG, SG 1 || SG 2 may no longer be a subgraph of SG.


We emphasize that Propositions 1-2 hold without blowing up the number of symbols.

we provide a free distribution of our parsers along with this paper, as part of the TurboParser toolkit.


Crabbé and Seddah (2014) is the best non-reranking system in the shared task, and Björkelund et al.

reported runtimes are still superior to 10 seconds per sentence, which is not practical.


Below we introduce two different projection methods, which are orthogonal and later combined hierarchically.

it does not use probability and the local score is just φ(a i , p i−1 ) = θ f (a i , p i−1 ).


We have devised a new method that enables optimal search for the structured perceptron, but it cannot handle even modestly large feature templates.

the LF takes more time to calculate    Zhang and Clark (2009).


To this end, we propose tree approximation to obtain CCG sub-graphs under the factorization using tree parsing algorithms.

ccGbank is a translation of the Penn Treebank into a corpus of ccG derivations (Hockenmaier  Table 1: The accuracy of the POS tagger and the UAS of the syntax tree parsers.


• The FFN-based models, on the other hand, avoid this difficulty by feeding directly on the history.

competitor Models we compare gencNN to the following competitor models • 5-gram: We use SRI Language Modeling Toolkit (Stolcke and others, 2002) to train a 5-gram language model with modified Kneser-Ney smoothing; • FFN-LM: The neural language model based on feedfoward network (Vaswani et al., 2013).


Empirical study shows that NRM can generate grammatically correct and content-wise appropriate responses to over 75% of the input text, outperforming stateof-the-arts in the same setting, including retrieval-based and SMT-based models.

figure 9 lists 5 responses to the same post, which are gotten with beam search with beam size = 500, among which we keep only the best one (biggest likelihood) for each first word.


The weight larger than 1 is given to the concept occurrences in the first few paragraphs.

then, sentence fusion is applied within each cluster of related sentences to generate a new sentence containing common information units of the sentences.


As mentioned above, we favor the pyramid evaluation over the ROUGE score because it can measure the summary quality beyond simply string matching.

the adaptability of these methods is limited to some extent.


(2) To simulate compression-based summarization, we can adapt our framework to conduct sentence selection and sentence compression in a joint manner.

we form a raw sentence with a selected NP as the subject followed by the corresponding selected VPs that are indicated by sentence generation indicatorγ ij having the value 1.


Meanwhile, since the basic unit is phrases, we design compatibility relations among NPs and VPs, as well as other optimization constraints, to ensure that the generated sentences contain correct facts.

the selected sentences may still contain unimportant or redundant phrases.


We evaluate the systems by Mean Average Precision at k (MAP@k) for k = 5, 10, 15, 20 over all four events.


The longer ago an event happened the more likely it is to have been forgotten.

their dataset is not publically available.


All system salience and similarity threshold parameters are tuned on the development set to maximize ROUGE-2 F1 scores.

temporal Relevance Features As we track events over time, it is likely that the coverage of the event may die down, only to spike back up when there is a breaking development.


generally lower preferences will result in fewer clusters.


In this paper we propose a framework for automatic analysis of a large collection of political texts.

after consolidation annotators agreed on all topic annotations.


Understanding these theoretical concepts is necessary in laying the ground for our computational approach.

we demonstrate how the use of Bayesian methods and time series analysis captures the different ways in which political parties control the political discourse.


The term ( ) is the sum of probabilities of unseen referents that could be referred to by the pronoun 'he'.

it is not clear how this form-salience mapping holds nor why it should be.


For instance, if two players attempt to move into the same unoccupied territory, this is not necessarily aggressive: allies sometimes use this tactic ("bouncing") to ensure that a territory remains unoccupied.

while our focus is on a single popular game, we choose methods that generalize to other domains, revealing dynamics present in other social interactions (Section 6).


Table 3: Selected features for recognizing imminent betrayal, in decreasing order of the absolute value of their coefficients.

betrayals are established and reciprocal friendships that end with at least two hostile acts.


A major focus in computational social science has been the study of interpersonal relations through data.

to capture discourse complexity, we measure the average number of explicit discourse connectors per sentence (Prasad et al., 2008).


Such scenarios suggest an important research challenge: is the forthcoming betrayal signaled by linguistic cues appearing in the (ostensibly friendly) conversation between the betrayer and the eventual victim?

imminent betrayal is signaled by sudden changes in the balance of conversational attributes such as positive sentiment, politeness, and structured discourse.


NEARBYPERSON is assigned when the subject of the disease/symptom is a person whom the poster can directly see or hear.

example 3 in Figure 1 shows a case in which the subject is omitted.


NONE accounts for 14% of the entire corpus, but only 5% of the runny nose corpus.

in addition, they use a corpus that consists of newswire text, the writing style and word usage of which differ considerably from those of tweets.


(2013) aimed to improve the accuracy of detecting mentions of flu infections.

it is extremely difficult for annotators to judge from a tweet whether the person mentioned in the tweet is in the same prefecture as the poster.


If we further restrict our subset of sentences to only those with positive ground truth labels, we find that while both models struggle, the DRecNN obtains 41.7% accuracy, outperforming the DAN's 37.5%.

most previous work on neural network-based methods for NLP tasks explicitly model word order.


The corresponding value of k may differ from one graph to another.

edge direction was used by Filippova (2010) so as to extract valid sentences but not here in order to capture some word inversion.


In terms of subgraphs, this means words that co-occur with each other and form a dense subgraph as opposed to a path like for a ngram.

in our work, we explored a graph representation of text, namely graph-of-words, to challenge the traditional bag-of-words representation and help better classify textual documents into categories.


Under the current classic statistical machine translation framework, it is hard to share information across different phrase tables among different language pairs.

we will describe model details in this section.


We extend this into multiple languages setting.

we also compare our trained models with Moses.


Our model is a general framework for translating from one source language to many targets.

suppose we want to translate from English to many different languages, for instance, French(Fr), Dutch(Nl), Spanish(Es).


Traditionally, such scenarios call for dynamic programming for exact inference.

it is also interesting to study whether extending our model with deep architectures can benefit CWS.


This gating mechanism is effective to model the combinations of the characters.

the RNN need a topological structure to model a sequence, such as a syntactic tree.


Different with them, our model just has a small improvement with the bigram feature embeddings, which indicates that our approach has well modeled the complicated combinations of the context characters, and does not need much help of further feature engineering.

figure 5 gives the convergence speeds of the five models with different numbers of layers and the model with layer-wise training strategy on development set of PKU dataset.


The word clusters that were built from a reference corpus and then used as features in the GP classifier, give us the opportunity to extract some qualitative derivations from our predictive task.

we also observe that words in 'Corporate' (#124) are used more as the skill required for a job gets higher.


These two problems are not actually independent but interrelated, because knowing the part-of-speech of some infrequent or unknown word will give contextual clues to word segmentation, and vice versa.

we can see that our PYHSMM outperforms on all the datasets.


For the transition probability of hidden states, we put a HPY process prior as (Blunsom and Cohn, 2011): with the final base measure being a uniform distribution over the states.

to model this property, NPYLM assumes that word prior G 0 is generated from character HPYLM to model a well-formedness of w. In practice, to avoid dependency on n in the character model, we used an ∞-gram VPYLM (Mochihashi and Sumita, 2008) in this research.


For our model, note that Negative Binomial is obtained from a Poisson distribution Po(λ) whose parameter λ again follows a Gamma distribution Ga(r, b) and integrated out: This construction exactly mirrors the Poisson-Gamma word length distribution in (Mochihashi et al., 2009) with sampled λ.

to sample words and states (part-of-speech) jointly, we first compute inside probabilities forward from BOS to EOS and sample backwards from EOS according to the Forward filtering-Backward sampling algorithm (Scott, 2002).


Our model works best when M ′ = 5K, which is slightly better than the case of M ′ = 1K/20K.

when M ′ = 1K, the accuracy is also inferior to the case of M ′ = 5K, which indicates that PD is not effectively utilized in this setting.


We also present the results of the baseline model trained using 5K sentences in one iteration for better comparison.

from the trend of the curves, we expect that the accuracy gap between our coupled model with M ′ = 5K/20K and the baseline model should be much smaller when reaching convergence.


The learning objective becomes: The hidden layer S of the autoencoder gives us synset embeddings.

mFS (line 8) is the most frequent sense baseline.


A simple baseline is to use the underlying word2vec embeddings directly (line 5).

the dataset is the closest we could find for sense similarity.


To illustrate, Figure 3 depicts a pair of systems for which the baseline system appears to outperform the other when evaluated with MAE, but this is only due to the conservative variance in its pre- diction score distribution, as can be seen by the narrow blue spike in Figure 3(b).

we propose what may correspond more directly to translation quality is an alteration of this, a post-edit rate (PER), where PETs are normalized by the number of words in translations.


Although being targeted at building semantic similarity datasets, the algorithm is task-independent, so it may also be used for any task which measures any kind of relation between two linguistic items in a numerical way.

a final adjudication was performed accordingly.


Hassan and Mihalcea (2009) built two sets of cross-lingual datasets by translating the English MC-30 (Miller and Charles, 1991) and the WordSim-353 (Finkelstein et al., 2002) datasets into three languages.


Finally, all three translators met and performed a final check, with specific focus on the following two cases: (1) duplicate pairs in the dataset, and (2) pairs with repeated words.

due to the lack of standard multilingual benchmarks, word similarity systems had in the main been limited to the English language (Mihalcea and Moldovan, 1999;Agirre and Lopez, 2003;Agirre and de Lacalle, 2004;Strube and Ponzetto, 2006;Gabrilovich and Markovitch, 2007;Mihalcea, 2007;Pilehvar et al., 2013;, up until the recent creation of datasets built by translating the English RG-65 dataset (Rubenstein and Goodenough, 1965) into French (Joubarne and Inkpen, 2011), German (Gurevych, 2005), and Portuguese (Granada et al., 2014).


The FSE algorithm is implemented in various flavours.

as the caching system stores shortest path between nodes, these computed values are more likely to be a subpath of another query when the number of queries grows.


The training error propagates back to fine-tune the parameters of the CNN and the input word vectors.

this paradigm usually has two limitations: (1) The performance of these approaches is sensitive to the clustering algorithm which requires the setting of the sense number for each word.


Finally, we assess the learned negation function by applying it to an adjective and computing accuracy in the task of retrieving the correct antonym as nearest neighbour of the not-composed vector, searching across all Word-Net adjectives (10K items).

our formulation is inspired by Lazaridou et al.


mLCM also shows promising results on the task of learning a compositional negation operator mapping adjectives to their antonyms.

by adopting the framework of , we take not to be a matrix that, when multiplied with an adjectiverepresenting vector, returns the vector of an adjective with the opposite meaning.


Here, the baseline approach is the supervised learning approach by using only the initial labeled data (i.e.

we apply metalearning to predict the unlabeled data given the outputs from the member algorithms and propose N-fold cross validation to guarantee a suitable size of the data for training the meta-classifier.


When the hidden states {H t } are obtained, the second phase estimates the remaining parameters for the neural network model in a supervised prediction problem.

it is also capable of taking the ordering of words into account, and collecting information from arbitrary features associated with the context.


Experimental results show that it is feasible to use text-only features to accurately predict helpfulness scores.

cross-category test further shows the models trained with semantic features are easier to be generalized to reviews of different product categories.


GALC is excluded in this analysis due to its poor performance earlier.

reviews need to be ranked and recommended to consumers.


Indeed, we find that all methods are often outperformed by phrase-count logistic regression with rare-feature up-weighting and carefully chosen regularization.

d C , leading to C distinct language representations V 1 .


4) News articles from CNN/USAToday that have more than 100 explicitly linked tweets were kept.

table 2 shows the overall performance 5 .


(2014), but the difference is not statistically significant.

the complexity of the word is estimated by its information content (ic), computed as follows: w ∈C freq(w ) + 1 where freq(w ) is the frequency of the word w in a large corpus C, which, in our case, was the Google Book Ngrams corpus (Michel et al., 2011).


Our findings correlate with previous empirical work in the field, and show that discrimination may effectively trump the inherent preference for absolute properties and for those that are easier to realise in surface form.

results along these lines are also reported in (Tarenskeen et al., 2014).


A graphical representation of one link in the parsing model is shown in Figure 1.

the PDtB consists of annotated articles from the Wall Street Journal and is used in our empirical evaluations.


When automatically detecting (dis)agreement, these features fall short, reaching around 65% accuracy on a balanced dataset (Abbott et al., 2011;Misra and Walker, 2013).

we also thank Xuchen Yao for his tremendous help with the Jacana aligner.


The first line of approaches attempts to generate novel captions directly from images (Farhadi et al., 2010;.

as a step towards achieving this goal, in this paper, we introduce a novel automatic query expansion approach for image captioning to retrieve semantically more relevant captions.


(2015), who try different ways of modulating the inclusion of perceptual input in their multi-modal skip-gram model, and find that the entropy of the centroid vector µ works well (where p(µ j ) = µ j || µ|| and m is the vector length): We calculate the directionality of a hyponymhypernym pair with a measure f using the following formula for a word pair (p, q).

dK and LR are supported by EPSRC grant EP/I037512/1.


This property has led to the introduction of lexical entailment measures that compare the entropy of distributional word representations, under the assumption that a more general term has a higher-entropy distribution (Herbelot and Ganesalingam, 2013;Santus et al., 2014).

since even cohyponyms will not have identical values for f , we introduce a threshold α which sets a minimum difference in generality for hypernym identification: s(p, q) > 0 iff f (q) > f (p) + α, i.e.


This highlights the importance of Kendall's τ maximization in the simple discriminative preordering model.

given an integer list measures a similarity between x and sorted x as: where c(x) is the number of concordant pairs between x and sorted x, which is defined as: and 0 otherwise.


All the students from school B (82 students) were first-year students.

a largescale experiment involving 320 subjects revealed that the context-unawareness of the current MT systems severely damages human performance when solving the test problems, while one of the evaluated MT systems performed as good as a human translation produced in a context-unaware condition.


These findings suggest that there is an inherent discrepancy between the assessment of the overall translation quality of a problem and the CAR.

the agreement rates are still less than 0.7 for all pairs of compared systems.


Automatic machine translation (MT) evaluation metrics measure the quality of the translations against human references.

we set n equal to 2 for the one-hot n-gram representation, the vector size of the recursive auto-encoder to 10, and the vector size of word embeddings to 80.


In our studies here, we first compute the sentence-level one-hot, word embedding, and RAE representations.

training the distributed representations only needs a reasonable amount of monolingual, unlabeled data that is not necessary drawn from the test domain.


Given a document collection (data) {V d } T d , and another collection (noise) {V n } Tn with T n = kT d , NCE distinguishes these (1+k)T d documents simply based on Bayes' Theorem, where we assumed data samples matched by our model, indicating P θ P data , and noise samples generated from an artificial distribution P n .

in this paper, we propose an efficient learning strategy for RSM named α-NCE, applying NCE as the basic estimator.


Precision-recall (P-R) curves and mean average precision (MAP) are two metrics we used for evaluation.

as α-NCE learns RSM by distinguishing the data and noise from their respective features, parameters are trained more like a feature extractor than a generative model.


Using a few different NLP tasks, we then empirically show that our method can indeed learn a better classifier for the target domain than a few baselines.

domain adaptation aims to use labeled data from a source domain to help build a system for a target domain, possibly with a small amount of labeled data from the target domain.


Note that this unified form also includes SkipGram with noise contrastive estimation (SGNCE) (Mnih and Kavukcuoglu, 2013), which approximates m i,j = log( c i,j kc j ) in matrix factorization view.

table 4 shows the training time and performance results gained from our benchmark data.


2 can be rewritten as follows by a simple reformulation: Here, let us substitute e i • o j in Eq.

we can obtain the following relation for SGNS: Note that the expectation of c i,j is |D| if the negative sampling is assumed to follow unigram probability c j |D| , and the negative sampling data is k-times larger than the training data D, where The above matches 'shifted PMI' as described in  when we substitute c i,j for In addition, the word2vec implementation uses a smoothing factor α to reduce the selection of high-occurrence-frequency words during the negative sampling.


We also propose a word clustering technique based on canonical correlation analysis (CCA) that is sensitive to multiple word senses.

we can exploit context embeddings to infer word senses.


In this paper, we create a labelled dataset of 2 million clues on which we apply an innovative Distributional Neural Network (DNN) for reranking clue pairs.

(2014) (answer sentence selection in Question Answering) with the following differences: (i) to more complex convolutional sentence models explored in (Hu et al., 2014) and in (Yu et al., 2014), our sentence model is composed of a single averaging operation.


Figure 1: Two topically different NNM retellings with similar free recall scores (6 and 5, respectively).

aSD was diagnosed via clinical consensus according to the DSM-IV-TR criteria (american Psychiatric association, 2000) and the established thresholds on two widely-used diagnostic instruments: the autism Diagnostic Observation Schedule (Lord et al., 2002) and the Social Communication Questionnaire (Rutter et al., 2003).


In a modified version ofWordNet-based vector similarity, Sim WV ), (Li et al., 2006), we first create vectors v n and v m for each narrative n and m, where each element corresponds to a word in the type union of n and m. We assign values to each element e in v n using the following formulation: S(e, n) = 1 if e ∈ n max where LS is Lin's universal similarity (Lin, 1998).

it is our expectation that children with ASD do not only veer from the target topic more frequently than typically developing children but also pursue topics of their own individual specific interests.


All roles that have a common parent based on the Inheritance and Subframe relations will share a set of features in common.

daumé 2007proposed a feature augmentation approach that is now widely used in supervised domain adaptation scenarios.


Usually, the numbers of adjective superlatives and gradable KB predicates are large, so that it is impossible to craft mapping rules to cover every pair of adjective superlative and predicate.

additionally, we manually annotate 77 declarative sentences covering 24 Freebase predicates from WSJ and Wikipedia as the declarative dataset.


It turns out that the best olfactory representations are obtained with 30 dimensions.

these datasets consist of concept pairs together with a human-annotated similarity score.


RNN and NN both have 100% coverage on the Wikipedia data.

the following recurrence 2 is used to compute the activations of the hidden state at word position t: where f is a non-linear activation function; here we use the sigmoid function f (z) = 1 1+e −z .


This makes accurate disambiguation of lexical types much more challenging.

in addition to improving parsing efficiency, supertagging also has a large impact on parsing accuracy (Curran et al., 2006;Kummerfeld et al., 2010), since the derivation space of the parser is determined by the supertagger, at both train-*All work was completed before the author joined Facebook.


note that the for loop runs in Algorithm 1 Computation of the loss of a configuration.


This extra robustness in training provides higher parsing accuracy.

note that this results in removing exactly n c (A ∪ I(c, t G )) arcs, as we have shown that the cycles in A ∪ I(c, t G ) are disjoint.


When the algorithm is in a configuration λ 1 |i, λ 2 , j|B, A , we will say that it is considering the focus words i and j, located at the end of the first list and at the beginning of the buffer.

if each arc in a tree-compatible set is individually reachable from configuration c, then that Shift: Right-Arc: only if k | k → j ∈ A (single-head) and j → * i ∈ A (acyclicity).


moreover, Sun and Xu (2011) attempted to extract information from large unlabeled data to enhance the Chinese word segmentation results.


Most Open IE systems employ syntactic information such as parse trees and part of speech (POS) tags, but ignore lexical information.

our modified kernel is based on the SubSet Tree (SST) Kernel proposed by Collins and Duffy (2002).


For a disorder mention containing numbers between one to ten, new forms are produced by replacing each number in the mention with other forms of the same number.

the reference ontology used is the MEDIC lexicon (Davis et al., 2012), which contains 11,915 disorder concepts.


Finally, this work challenges prior claims that spoken language is "more complex" than other genres with regards to referentiality.

our meta-analysis shows that more linguistically motivated local features such as PoS tags and dependency relations are substantially overshadowed in effectiveness by lexical and discourse features, although this may be due in part to reduced performance of these automatic taggers on the more colloquial language in online reviews.


The idea is to resolve AZPs by using a probabilistic pronoun resolution model trained on overt pronouns in an unsupervised manner.

so, Next, applying Bayes Rule to P (l=1|k c ), we get: Representing k c as a set of n features f 1 c , .


Let |M| be the number of operations for one matrix multiplication.

we propose an efficient algorithm, Co-Simmate, to speed up all pairs Co-Simranks retrieval from O(log(1/ǫ)n 3 ) to O(log 2 (log(1/ǫ))n 3 ) time, to attain a desired accuracy ǫ.


Decisions relating to relevance of material to a given topic (MO question) are delegated to experts on the website.

answers" vary in quality (e.g., due to instances of bias or obscenity).


Understanding the search intents of queries is essential for satisfying users' information needs and is very important for many search tasks such as personalized search, query suggestion, and search result presentation.

(2) is picked from10 -2 to 10 4 using a 10-fold cross validation procedure.


To evaluate the performance of our approach, we employ the measures in (Luo, 2005), which are computed as follows, where R ' is the predicted partition and R is the ground-truth partition; π(A, B) is a similarity measure between set A and B, which is Jaccard coefficient in this paper; and g(.)

for a given query q, we collect its reformulations with additional words from query log as query subtopic candidates, e.g., we collect {'jaguar sports car', 'jaguar XJ sport', 'jaguar diet', …} for query 'jaguar'.


We evaluate our methods on 3 categories, i.e., English "earthquake" and "election" categories containing 293 and 60 articles, and Chinese "earthquake" category containing 48 articles 5 .

6 We use an incremental clustering algorithm (λ = 0) (Pro+S), and considering both structural and textual information (0 < λ < 1) (Pro+ST).


For each subtopic relation (t i , t j ) ∈ R a , we can calculate a count/weight n(t i , t j ), representing the number of articles in A c containing the relation.

our input consists of a set of Wiki articles A c = {a}, belonging to a Wiki category c. As shown in Fig.1, each article a ∈ A c contains a CT (topic tree hierarchy) where T a is a set of topics, each denoted by a tag g and associated with a text description d g , and The output is an integrated comprehensive CTH H c = {T c , R c } where T c = {t} is a set of topics, each denoted by a set of tags t = {g} and associated by a text description d t aggregated by {d g } g∈t , and We map the problem of learning the output H c from the input {H a }, a ∈ A c , as a structure learning problem.


The meaningful SUs are assumed that they have one close neighbor at least in embedding space.

(2013) proposed the Recursive Neural Network (RNN) that has been proven to be efficient in terms of constructing sentences representations.


Our TimeLine extraction system uses the linguistic information provided by the pipeline.

furthermore, time-anchoring is just a part of the works presented above.


We also set some constraints to select certain events according to the specification of the SemEval task.

for that, we have proposed a simple strategy that acquires implicit relations and it obtains a more complete time-anchoring.


For each token x i , the vectors obtained from the three look-ups above are concatenated into a single vector x i to represent the token.

in practice, we initialize this table randomly.


As mentioned in the Introduction, our method (Section 3) is based on event-independent features that identify similarities between a candidate trigger and a given seed list.

our method is evaluated on the ACE-2005 dataset, achieving 5.7% F 1 improvement over a state-of-the-art supervised system which uses the full training data.


Current methods remain largely untested in this setting, despite downstream tasks in Chinese that rely on name matching (Chen et al., 2010;Cassidy et al., 2011).

because of the issues mentioned above we expect these to perform poorly when applied to Chinese strings.


Our goal is to transfer each feature from L S to L T space.

regular expressions to capture certain patterns such as products model numbers), latin-only features (first-is-capital, allcapitals, all-small); 3) In-Title: A binary feature that specifies whether the token is in the product's title or description.


Named Entity Recognition (NER) is usually solved by a supervised learning approach, where sequential labeling models are trained from a large amount of manually annotated corpora.

(2011) trained de-lexicalized dependency parsers in English and then "re-lexicalized" the parser.


For the future work, we would like to investigate the generality of our approach in broader languages and domains.

color Brand Material Model Type Size  EN 358  814  733  203  1238 427  ES 207  425  301  172  606  126  ZH 416  60  381  24  690  306   Table 1: Language-Tags Numbers Stats the length of product descriptions ranges from 12-130 words.


However, at the era of big data outliers are inevitable due to the ever increasing data sizes.

we use the dataset from ACL-IMDB provided by (Maas et al., 2011).


To be more specific, in many real-world problems a pair of object can be related in several different ways, which inevitably complicates the problem and calls for new clustering algorithms for better understanding to the data.

at the era of big data outliers are inevitable due to the ever increasing data sizes.


We refer to Dasgupta and Ng (2009) where the authors report that training SVM with unambiguous data alone produces rather inferior result.

we seek to learn the writing styles or other indicative patterns -topics of interest, word choices etc.


For instance, the word Kohle maps to Kohle (coal) and Kohl (cabbage).

we have also discovered that we still need to improve IWNLP to get better results for the lemmatization of verbs and adjectives.


Our predictive analysis shows that the orthographic cues are different for cognates and borrowings, and that underlying linguistic factors captured by our model, such as affixes and diacritics, are indicative of the type of relationship between the words.

we consider words as input sequences and we use a very simple substitution matrix 1 , which assigns  Table 2: Statistics for the dataset of related words.


Here, we account for the etymology of the words, and we adopt the following definition: two words form a cognate pair if they share a common ancestor and have the same meaning.

equal scores to all substitutions, disregarding diacritics (e.g., we ensure that e andè are matched).


For IT-RO both distances are small (0.26 and 0.29), as opposed to the other languages, where there is a more significant difference between the two (e.g., 0.26 and 0.52 for ES-RO).

in problems of language classification, distinguishing cognates from borrowings is essential.


Finally, Figure 2 shows how Spearman's ρ is affected along three dimensions of study.


1969) has also been proven useful in building lexicographic taxonomies (Amsler, 1980) and identifying synonyms (Wang and Hirst, 2011).

lexical similarity results on the 17M corpus are listed in Table 2.


It contains 4 billion words from news articles, parsed by the Stanford Parser.

we propose a relation-dependent model to predict dependents given a governor under individual dependency relations.


Although distributional word vector dimensions cannot, in general, be identified with linguistic properties, it has been shown that some vector construction strategies yield dimensions that are relatively more interpretable (Murphy et al., 2012;Fyshe et al., 2014;Fyshe et al., 2015;Faruqui et al., 2015b).

we thank Nathan Schneider for giving comments on an earlier draft of this paper and the anonymous reviewers for their feedback.


A part of the work for improving such thesauri focuses on the filtering of the components of the distributional contexts of words (Padró et al., 2014;Polajnar and Clark, 2014) or their reweighting, either by turning the weights of these components into ranks (Broda et al., 2009) or by adapting them through a bootstrapping method from the thesaurus to improve (Zhitomirsky-Geffet and Dagan, 2009;Yamamoto and Asakura, 2010).

such idea faces two problems: first, detecting "good" entries; second, learning a model from them for improving the performance of the other entries.


The RightAP factor shows a consistent postnominal preference, positively correlated to its length.

a much more detailed study of the lexical and structural properties of this effect is developed in 4 a log-likelihood test of the model including RightaP, LeftaP and RightNP factors compared to the model including only RightaP and LeftaP factors yields χ 2 = 107 and p < .001.


There is no need therefore to minimize the external dependency to the noun, since the noun phrase can be entirely predicted based on its left corner.

we first convert all treebanks to coarse universal part-of-speech tags, using existing conventional mappings from the original tagset to the universal tagset (Petrov et al., 2012).


There are no mis-tagged emoticons in the older group, who generally uses fewer emoticons (see also Hovy et al.

nouns often get mistagged as VERB, usually because of homography due to null-conversion (ordering, face, needs).


For example, the long short term memory (LSTM) RNN (Hochreiter, 1997) is an enhanced architecture to implement the recurrent feedbacks using various learnable gates, and it has obtained promising results on handwriting recognition (Graves, 2009) and sequence modeling (Graves, 2013).

it is widely reported that RNN-LMs usually outperform FNN-LMs in language modeling.


the first 3 are all in the prophetic literature and the other two are in the wisdom literature.


Probabilistic topic model such as Latent Dirichlet Allocation(LDA) (Blei et al, 2003) has been widely used for discovering latent topics from document collections by capturing words' cooccuring relation.

eighty percent data are used for training, others for testing.


The Back-Propagation Through Time (BPTT) variant of SGD enables to sum-up gradients from consecutive time steps before updating the parameters of the RNN and to handle the long-range temporal dependencies in the hidden s and output y sequences.

each word w i is assigned to a class c i and the corresponding log-likelihood is effectively log where j is the index of word w i among words belonging to class c i .


When modeling a sentence using an RNN, the state s j that is used to generate the distribution of words w i (where j is the parent of i in the tree), represents the vector embedding of the history of the ancestor words A(w i ).

furthermore, we explain how we can incorporate the dependency labels as features.


This corresponds to a situation, e.g., when a journalist analyses a rumour during short spot checks, but wants to know the prevalence of the rumour at other times, thus limiting the need for constant attention.

these models were mainly used for network modelling rather than revealing complex temporal patterns, which may emerge only implicitly, and are more limited in the kinds of temporal patterns that may be represented.


In terms of performing distributed representation learning for output variables, our proposed model shares similarity with the structured output representation learning approach developed by Srikumar and Manning (2014), which extends the structured support vector machines to simultaneously learn the prediction model and the distributed representations of the output labels.

the corresponding distributed state representation vectors can be obtained as We then train a supervised NLP system (e.g., POS tagging or NP chunking) on the labeled source training sentences using the distributed state representations as augmenting input features and perform prediction on the augmented test sentences.


Domain adaptation aims to obtain an effective prediction model for a particular target domain where labeled training data is scarce by exploiting labeled data from a related source domain.

they use standard HMMs to produce discrete hidden state features for each observation word, while we induce distributed state representation vectors.


In this "ideal", error-free scenario the situation slightly changes (unsurprisingly, the "% wins of colored" slightly increases, especially for long suggestions for which we have the highest number of misclassifications), but the overall conclusions remain the same.

so far, despite the potential practical benefits, the progress in QE research has not been followed by conclusive results that demonstrate whether the use of quality labels can actually lead to noticeable productivity gains in the CAT framework.


The configurations for decoding the ZODIAC-408 cipher as well as Part two of the BEALE ciphers are almost identical: For both setups we use an 8-gram character language model trained on a subset of the English Gigaword corpus (Parker et al., 2011).

the goal of UNRAVEL is not to provide a solver for every single encipherment method, but rather to provide reusable tools that can be applied to unsupervised learning for machine translation.


Although the 1,000-best oracle remains at the same level over the iterations, the 1,000best average score 5 increases by 2 BLEU at the last iteration over the first 1,000-best hypotheses produced by Moses, pointing out a strong improvement of the average quality of the 1,000best hypotheses.

2 Iterations are performed until no more improvements of the BLEU score are obtained by Rerank on a development set.


These methods are useful in capturing semantic information carried by high-level units (such as phrases and beyond) and usually do not rely on word alignments.

we can also leverage word alignments and define CLC+wA (Cross-lingual context with word alignments).


Among various methods using pivot languages, the triangulation method (Cohn and Lapata, 2007;Utiyama and Isahara, 2007;Zhu et al., 2014), which translates by combining source-pivot and pivot-target translation models into a source-target 1 Code to replicate the experiments can be found at https://github.com/akivajp/acl2015 model, has been shown to be one of the most effective approaches.

in this paper, we propose a novel approach to remember the pivot phrases in the triangulation stage, and use a pivot language model as an additional information source at translation time.


When MusicBrainz returns multiple results for a given query, we simply select the first result.

our literature review of this area suggested that the aspects being considered in readability assessment are sufficiently different from the dimensions that seem to be most relevant for media age appropriatness ratings.


We here use CBOW for radical embedding because the two methods exhibit few differences, and CBOW is slightly faster in experiments.

we evaluate the performance using two standard datasets: PKU and MSR, as provided by (Emerson, 2005).


95,502,506 triplets are used for training, with the rest 137,805 triplets as testing.

it is hard to decipher the semantics of strokes, and radicals are the minimum semantic unit for Chinese.


The fragment categories are disregarded in this evaluation and the system's result is considered correct even if its output category does not match the one marked by the annotators.

using the POS tags produced by the POS tagger instead of the ones produced by the parser, the "Parse + Tag" model achieved a precision of 0.84, a recall of 0.62 and an F 0.5 of 0.78, improving upon the results of the "Parse" model and significantly outperforming all four baselines 5 .


There are two sets of features: (a) N-gram features, and (b) Stylistic features.

some categories of errors that occur are: 1.


An application that identifies potential crimes must work with high precision, since the target population to be monitored will be large.

the ubiquity of communication devices has made social media highly accessible.


We refer to the the training process proposed in Figure 2 (b) as "variational corpora".

the probability mass on each x i is proportional to its frequency in W and then normalized by softmax: .


By summing up the costs of all necessary operations, the total cost σ(S) = ∑ k i=1 σ(s i ) defines the matching degree of T 1 and T 2 .

multi-level PAM is capable of revealing interconnection between sub-level nodes by inferencing corresponding super-level nodes.


We establish the language model smoothed with both text information and social factors.

for π u , the most intuitive way is to calculate the contacts similarity of the social network users, i.e., friends or followees in common.


Generally, given a non-smoothed document language model P (w|d), which indicates a word distribution for a term w in document d, we attempt to generate a smoothed language model P (w|d + ) that could better estimate the text contents of a document d as d + to avoid zero probabilities for those words not seen in d. Arbitrary assignment of pseudo word counts such as add-λ to every unseen words once was a major improvement for language model smoothing (Chen and Goodman, 1996).

here we propose an optimization framework with regularization for language model smoothing on social networks, using both textual information and the social structure.


Provide a annotated keyword annotated dataset consisting of 1827 tweets.

the system extracts a list of candidate keywords from a document and trains a decision tree over a large set of hand engineered features, also including tF-IDF, in order to predict the correct keywords on the training set.


There are many methods that have been proposed for keyword extraction.

instead, we will used unsupervised methods that leverage large amounts of unannotated data.


As baselines, we reported results using a TF-IDF, the default MAUI toolkit, and our own implementation of (Li et al., 2010) framework.

we define the number of keywords in a tweet as the ratio between number of words in the tweet and k, which is maximized on the development set.


(2) Negation is an important feature for our task.

(3) When negation is concerned, opposition features are among the most productive.


(Gonzalez-Ibanez et al., 2011) exploited the common ground between speaker and hearer by looking if a tweet is a reply to another tweet.

even though the algorithm improves the classifier performance, the number of queries is small which suggests that a much larger dataset is needed.


In addition, we will study predicting the tense of multiple predicates in a sentence and identifying imperative sentences in a conversation, which is also a challenge of tense prediction.

moreover, sentences in a conversation often omit important sentence components.


For example, the subject and the object of the following sentences have the same person role but have different tenses because "警告(warn)" is the current action of the speaker but "教(teach)" is not.

when we are not sure which tense of the main predicate in a sentence should be, we can consider the tense of the entire scene.


The fact that the schema is meant to capture only the meanings of overt, non-root affixal morphemes restricts the semantic-conceptual space that must be captured by its features and renders an interlingual approach to representing inflectional morphology feasible.

the mere presence of this attribute was not a reliable indicator since some tables, such as those for Latin nouns (e.g.


Moreover, we empirically observed that the likelihood of some comment being good decreases with its position in the thread.

since the task consists in identifying good answers for a given question, further figures at the question level are necessary, i.e., we compute the target performance measure for all comments of each question and then we average the results over all threads (ta).


Its has two free parameters: k1 to tune termfrequency saturation; and b to calibrate the document-length normalization.

additionally, we compare BOW-CNN with six well-established IR algorithms available on the Lucene package (Hatcher et al., 2004).


7 These three results are obtained from files at http://research.microsoft.com/en-us/ um/redmond/projects/mctest/results.html.

after this transformation of the question (and a candidate answer) to a statement, we measure its similarity to the sentence in the window using simple dependency-based similarity features.


7 We also compare to concurrently-published results (Narasimhan and Barzilay, 2015;Sachan et al., 2015).

the formulaic semantics underlying these synthetic texts allows for the construction of perfect rule-based question answering systems, and may not reflect the patterns of natural linguistic expression.


For each time step in the BLSTM layer, the hidden vector or the output vector is generated by combining the cell memory vectors from two LSTM of both sides.

, x T ), a conventional RNN updates the hidden vector sequence h = (h 1 , h 2 , .


8: Here, (y 1 , ..., y T ) is the corresponding label sequence for the input sequence (p 1 , ..., p t−1 ), and the class distribution p(y t |c, y 1 , ..., .y t−1 ) is represented by a softmax function.

at each step, our approach uses the pre-trained word embeddings to encode the sentences of Qa pair, which then is used as the input vectors of the model.


Beyond typical features, Shah and Pomerantz (2010) trained a logistic regression (L-R) classifier with user metadata to predict the quality of answers in CQA.

mDBN (Hu et al., 2013): to DBN, multimodal DBN learns the joint representations of textual features and non-textual features rather than bag-of-words.


For example, white is a salient property of swan, a less salient property of house, and not a property at all of hammer.

the empirical success of DMs may not be uniform across the full range of semantic knowledge.


RHT (Lu, 2014) is the discriminative semantic parsing system based on relaxed hybrid trees.

we used a standard script to construct Prolog queries based on the outputs, and used the queries to retrieve answers from the GeoQuery database.


In early experiments, Naive Bayes performed comparably to or outperformed SVM because the dimensionality of the feature space was relatively low.

we achieve a 53.71% F 1 -score by adding features extracted from the preceding and the subsequent utterances, which is about a 10% improvement from a baseline classifier using only the features from the question itself.


Thus, the past work detects sarcasm with specific indicators.

subjective polarity: The tweet 'Yay for 3 hour Chem labs' is tagged by the author as sarcastic, which may not be common perception.


Note that the 'native polarity' need not be correct.

the past work detects sarcasm with specific indicators.


The output will be generated only if it reaches the maximum probability as follows (Brown et al., 1990;Zhao et al., 2009): Sentimental information is very useful in emotion detection (Gao et al., 2013).

bridging the gap between different languages becomes essential for emotion detection in code-switching texts.


A typical approach is to collect a portion of historical reviews from each user to construct a shared training corpus .

to understand the effect of global model training in model adaptation, we also included the performance of LinAdapt, which only used shifting and scaling operations and Cross feature grouping method with k = 400 (detailed feature grouping method will be discussed in the next experiment).


Following the solution proposed by Wang et al.

due to the limited availability of userspecific opinionated data, it is impractical to estimate independent models for each user.


(2011) trained a linear regression model for each seed cluster, and then predicted the VA rating of an unseen word using the model of the cluster to which the unseen word belongs.

future work will focus on extending the VA prediction from the word-level to the sentence-and document-levels.


Although the pagerank algorithm has been used for polarity ranking, it can still be extended for VA prediction.

second, whereas pagerank assigns an equal weight to the edges connected between an unseen word and its neighbor nodes, we consider their similarities as weights to construct a weighted graph such that neighbor nodes more similar to the unseen word may contribute more to estimate its VA ratings.


Let G=(V, E) be an undirected graph, where V denotes a set of words and E denotes a set of undirected edges.

this study extends the idea of pagerank in two aspects.


We adopt this RNN framework as the starting point for the work described here.

the latter model is much better at balancing its performance across all six domains, achieving the highest geometric mean and still improving over all but one of the domain-specific models.


In this paper, we propose a novel DM scheme based on sentence clustering.

the steps of sentence clustering are: Step 1: Initially, we only utilize the internal information to cluster the sentences via Affinity Propagation (AP) algorithm (Brendan et al., 2007) and denote the clustering result as C 0 .


the eye-tracking features are not contributing in those scenarios.


The system with added phrasal deletion achieved the BLEU score of 60.46, while the the standard model without phrasal deletion achieved the BLEU score of 59.87.

we focus on the two TS corpora available for English (wikipedia and EncBrit) and train a series of translation models on training and development datasets of varying size and quality.


Besides the features x p obtained through the CNNs, we also extract several documentdependent features notated as x e , shown in Table  1.

zeros and then apply a linear transformation using the weight w r to get a summary prior score x p .


The requirement of same arc label annotation is mainly needed for evaluation using the Labelled Attachment Score (LAS).

the error rate reduction is from 15.8% down to 6.5% for training data sizes from 1k to 15k tokens.


For each condition, we observe the same tendency in performance increases as Table 6.

a state is a triple (σ, β, R), where σ is a stack of identified concept subtrees, β is a buffer of words, and R is a set of identified relations.


To achieve this, we use a decoding method recently proposed for generative incremental parsing (Buys and Blunsom, 2015) based on particle filtering (Doucet et al., 2001), a sequential Monte Carlo sampling method.

to reduce the computational cost of normalising over the entire vocabulary, we factorize the probability as P (w|h) = P (c|t, h)P (w|c, t, h), where c = c(w) is the unique class of word w. For each c, let Γ(c) be the set of words in that class.


We do not convert to an F 1 score, as retaining the absolute number of mistakes is proportional to the size of the graph.

when trained in the traditional manner these systems are susceptible to the accumulation of errors when they find undesirable states during greedy decoding.


WASP also requires the implementation of an efficient algorithm for computing the conditional probability p(y | x).

generate a large dataset of constituency parse trees by taking sentences that multiple existing systems parse in the same way, and train a neural model on this dataset.


For example, the category Set might be divided into Records, Values, and Atomic when the knowledge source is a table (Pasupat and Liang, 2015).

the subset W divides Q into partitions F t = {q ∈ Q : q W = t} based on the denotation tuples t (e.g., from Figure 5, if W contains just w 2 , then q 2 and q 3 will be in the same partition F (China) ).


without a trigger "year"), and generating operations without a lexicon (e.g., generating argmax from "where's the longest competition").

forms not covered are due to limitations of the deduction rules.


We found that only 5% of the logical forms are split from the original equivalence classes.

unlike manually designed test suites, we do not yet know the correct answer for each fictitious world or whether a world is helpful for filtering out spurious logical forms.


Moreover, dropout is used for regularizing the model (Zaremba et al., 2015).

model output is shown for SEQ2SEQ (a, b) and SEQ2TREE (c, d).


questions are paired with Prolog-style queries.


In order to evaluate the quality of our proposed framework and its portability to a new language, we use TAC-KBP2013 English Slot Filling (ESF), TAC-KBP 2015 English Cold Start Slot Filling (CSSF) and TAC-KBP2015 Chinese Slot Filling (CSF) data sets for which we can compare with the ground truth and state-of-the-art results reported in previous work.

unfortunately, a high-quality parser for a new language is often not available because of languagespecific features.


We scan through every n-gram (n in the order 3, 2, 1) in Hausa documents to see if any of them match an English name based on the following multi-media language-independent low-cost heuristics.

for example, when a child is watching a cartoon and shifting between versions in two languages, s/he can easily infer translation pairs for the same con- cept whose images appear frequently (e.g., "宝宝 (baby)" and "螃蟹 (crab)" in "Dora Exploration", "海盗 (pirate)" in "the Garden Guardians", and "亨利 (Henry)" in "Thomas Train"), as illustrated in figure 2.


89.7% of these mentions can be linked to the KB.

based on entity priors we observe that "Bawa Abdullah Wase" appears frequently in the same contexts as "Nigeria" and "Kano", and thus we successfully project it back to the Hausa sentence: "Haka ma Bawa Abdullahi Wase ya ce akawai abun dubawa a kalamun  Table 3: Name Tagging Performance (%).


(2016) used the previous attentional context to represent implicit fertility and passed it to the attention model, which is in essence similar to the input-feed method proposed in (Luong et al., 2015).

our empirical study shows that the combined objective consistently worsens the translation quality while slightly improves the alignment quality.


Another argument for discontinuous phrases is that they allow the decoder to use larger translation units which tend to produce better translations (Galley and Manning, 2010).

in experiments, this model further improves our system.


Typically, trees used in SMT are either phrasal structures (Galley et al., 2004;Marcu et al., 2006) or dependency structures Xiong et al., 2007;Xie et al., 2011;Li et al., 2014).

note that we treat the adjacent previous subgraph differently from others since information from the last previous unit is quite useful (Xiong et al., 2006;Cherry, 2013).


Note that in the basic PB model, the phrase segmentation is not explicitly modeled which means that different segmentations are treated equally (Koehn, 2010).

the expressiveness of these models is confined by hierarchical constraints of the grammars used (Galley and Manning, 2010) since these patterns still cover continuous spans of an input sentence.


(2015) use an edge replacement grammar to translate dependency graphs which are converted from dependency trees by labeling edges.

one of the significant weaknesses in conventional PB models is that only continuous phrases are used, so generalizations such as French ne .


The robot will need to first create a semantic representation for the verb "fill" where "the cup" and "milk" are grounded to the respective objects in the environment .

then a symbolic planner 3 was applied to predict an action sequence A (p) based on this predicted goal state.


For example, a problematic training instance is "put the pillow on the couch", where the object grounding module cannot correctly ground the "couch" to the target object.

this paper presents an incremental learning approach that represents and acquires semantics of action verbs based on state changes of the environment.


take(x) and turn(x)), others have relatively lower results (e.g.

some children hypotheses can be inconsistent with their parents.


Using our proposed model, we observe improvements on two tasks, neural machine translation on the Europarl English to French parallel corpora and text summarization on the Gigaword dataset.

we observe that in this case we can make the model learn to point a word in the context and copy it to the target text, as well as when to point.


In their work, the authors of (Bahdanau et al., 2014) use full-length Rouge Recall on this corpus, since the maximum length of limited-length version of Rouge recall of 75 bytes (intended for DUC data) is already long for Gigaword summaries.

switching network d t : The switching network d t is an MLP with sigmoid output function that outputs a scalar probability of switching between l t and w t , and represents the conditional probability distribution p(z t |(y, z) <t , x).


The switching network, which is a multilayer perceptron in our experiments, takes the representation of the context sequence (similar to the input annotation in NMT) and the previous hidden state of the output RNN as its input.

at each time-step t, the softalignment mechanism first computes the relevance weight e tj which determines the contribution of annotation vector h j to the t-th target word.


Before evaluation, merged units were automatically decomposed in the form of flat subtrees using specific arcs as in , so all systems can be evaluated and compared at the token level.

while these cases can be represented in principle, the parsing model we propose will not be capable of processing them.


It is a slightly modified version of Goldberg and Nivre (2013)'s algorithm 3, an approach they called learning with exploration.

each v i (Section 2) is the embedding of the i th instantiated symbol of this list.


Discrete Polysemy Model Alternatively, the metaphorical and literal senses of an adjective may be distinct enough that averaging the two senses together in a single adjective matrix produces representations that are not well-suited for either metaphorical or literal phrases.

a lifetime of experience with literal and metaphorical language may suffice.


Each h N i is produced by iterating the following equations (without the subscript i): where r t is the reset gate, z t is the update gate, h t is the proposed update state at time t and denotes a component-wise product.

the decoder is a language model that is conditioned on the distributed representation and, in Sent2Vec, is used to "predict" the sentences surrounding the input sentence.


Idioms often present idiosyncratic behaviour such as violating selection restrictions or changing the default semantic roles of syntactic categories (Sporleder and Li, 2009).

the model representation is based on semantic relatedness features similar to those used earlier in (Sporleder and Li, 2009).


Furthermore, other work using Sent2vec have shown the capabilities of the Sent2Vec representations to capture features that are suited to various NLP tasks where semantics is involved (e.g., paraphrase detection and semantic relatedness ).

we can assume that the Sent2Vec distributed representation is also carrying information regarding its context (without the need to explicitly access it).


The scoring function is parameterized as and the VO and SVO embeddings are computed as as proposed by Kartsaklis et al.

by contrast, many of the previous studies on embedding-based methods combine different corpora into a single dataset, or use multiple corpora just separately and compare them (Hashimoto and Tsuruoka, 2015;Muraoka et al., 2014;Pennington et al., 2014).


By simultaneously considering the compositionality of the phrases, our method would be helpful in saving the composition models from having to be powerful enough to perfectly encode the non-compositional phrases.

we have found that most of the usage was its idiomatic meaning.


For a word w ∈ C, we shall learn a sparse vector x = (x 1 , x 2 , .

the number of parameters in the Embedding and Prediction subnets in neural LMs increases linearly with respect to the vocabulary size, which is large (table 1).


The weights of the output layer are also compressed in a same way as embeddings.

to filter out small coefficients around zero, we simply set a value to 0 if it is less than 0.015 • max{v ∈ x}.


Regarding the compression method proposed in this paper, we notice that LSTM-z,wb and LSTM-z,w yield similar performance to LSTM-z.

in this paper, we propose to compress neural language models by sparse word representations.


our corpus has around 6 million sentences with at least one annotated entity.


This was the Italian ship that was captured by Palestinian terrorists back in 1985.

our approach can also discover new argument roles for a given event type.


", "gun" is identified as an Instrument for "battle" event based on the AMR relation :instrument.

based on Hypothesis 1, we learn sense-based embeddings from a large data set, using the Continuous Skip-gram model (Mikolov et al., 2013).


For example, probabilistic latent semantic analysis (Hofmann, 1999) can be first performed followed by parametric embedding (Iwata et al., 2007).

the proposed approach has been evaluated on two datasets.


However, in practice we don't observe the latent variables Z and only have the incomplete data W .

here, β, γ, η, λ, χ, δ are priors, I is an identity matrix, and P (e|x m , Φ) is the probability of the tweet w m with coordinate x m belonging to the event e. It is defined as, It is calculated as the normalized Euclidean distance between a tweet w m and an event e. Using this equation, when the Euclidean distance between a tweet w m and and an event e is small, the probability that tweet w m belongs to event e becomes large.


In open-domain text, a sentence is typically not straightforwardly predictable from preceding text; if it were, it would likely not be stated.

modi and Titov (2014) describe a feedforward network which is trained to predict event orderings.


Thus, beyond understanding individual predicates (e.g., at the semantic role labeling level), there is a need to place them and their arguments in a global context.

for both models of SemLM, we study four language model implementations: N-gram, skipgram (Mikolov et al., 2013b), continuous bagof-words (Mikolov et al., 2013a) and log-bilinear language model (Mnih and Hinton, 2007).


It employs a supervised joint mention detection and co-reference framework.

the mapping is not complete and there are remaining PropBank frames.


Besides, we proposed a novel domain similarity measure based on sentiment graphs, and incorporated the domain similarities between target and different source domains into the domain adaptation process.

our approach is more suitable for sentiment domain adaptation than these generalpurpose multiple source domain adaptation methods.


We will promote these internal consistencies in our factor graph model ( §3) as soft constraints.

similar dynamics hold for the negative case.


opinion role induction (Wiegand and Ruppenhofer, 2015) and effect analysis (Choi and Wiebe, 2014).

from the NC agreement, we see that annotators do not frequently choose completely opposite polarities, indicating that even when they disagree, their disagreements are based on the degree of connotations rather than the polarity itself.


For example, Figure 2b shows directed faction edges, where one entity is likely to agree with the other's opinions.

in balanced states, entities on positive terms have similar opinions towards other entities and those on negative terms have opposing opinions.


Beyond previous work, this paper endeavors to more thoroughly study this issue, and has made substantial progress from the following perspectives.

for PA, the difference among the three uncertainty metrics is small.


Because AL experiments need to train many parsing models, we throw out all training sentences longer than 50 to speed up our experiments.

it is more reasonable that human are good at resolving some ambiguities but bad at others.


7 Moreover, the subgradient descent algorithm theoretically converges but we have no guarantee that this will happen in a realistic number of iterations.

even with heavy pruning we were not able to run it on sentences above 20 words.


For each language, we decided to use the most constrained combination of bounded block degree constraints and well-nestedness which covers over 99% of the data.

transition-based algorithms offer low space and time complexity, typically linear in the length of sentences usually by relying on local predictors and beam strategies and thus do not provide any optimality guarantee on the produced structures.


We consider arc-factored dependency parsing with well-nestedness and bounded block degree constraints.

this reduction is less rewarding and there are many more variables set to 0 than 1 in a MSA solution.


Our formulation can enforce both types of constraints or only one of them without changing the resolution method.

in order to retrieve an optimal solution in all cases, we embed the Lagrangian Relaxation of the problem within a Branch-and-Bound procedure (Land and Doig, 1960).


Fortunately, we can use information retrieval techniques to generate a query-specific set of topical documents.

we can then visualize where these terms lie in the global and local embeddings.


Thus, T-DSQA cannot clearly clarify the meaning of "put" in Q4.

the terminal layer of the convolutional neural subnetworks is a fully connected layer.


(2015) used a fisher kernel to model the fixed size representation of the variable length questions.

word hashing is character level representation of documents which takes care of OOV words and words with minor spelling errors.


Besides, the additional information from grounded entity knowledge helps the model outperform the ungrounded mentions, with a consistent margin of 1.0%-2.0% NDCG@5.

• We conduct extensive experiments on a large dataset.


Combining the context-based approaches and collaborative filtering, Torres et al.

in summary, the main contributions of this paper are three-fold: • We propose the task of news citation recommendation and construct a real-world dataset for this task.


Most of the features are extracted under both of the settings.

as shown in Table 3, the former brings much better performance on each metric compared to the latter.


In this section, we give detailed win-loss analysis for the models trained with NDCG@5 metric under headlines+lead paragraphs setting.

previous studies use string-based overlap (Xu et al., 2014), machine translation measures (Madnani et al., 2012), and dependency syntax (Wan et al., 2006; to model text similarity.


In another example shown in Table 8, ungrounded mention SNP, though detected, contributes little to supporting the ground truth pair.

the implicit semantics can be obtained from the word embedding trained on large scale corpus, and the explicit semantics through linking entity mentions to the grounded entities in a knowledge base.


7 It takes about two days to finish 50 training epochs (for results in Figure 6 and the last row of Table 6) only with two cores of an Intel i7-5960X CPU.

as reported in (Kummerfeld et al., 2015), the margin methods generally outperform both likelihood and perception methods.


Contemporary to this work, some neural models (Zhang et al., 2016a;Liu et al., 2016) also leverage word-level information.

on PKU dataset but a competitive result on MSR dataset, which can attribute to too strict maximum word length setting for consistence as it is well known that MSR corpus has a much longer average word length (Zhao et al., 2010).


However, we handle the linearization in data preprocessing so that the model sees only reordered word-vector inputs.

each window is treated like a sentence in the previous subsection, but we include a location-based weight λ(k).


(2015) proposes a dialog system to augment a knowledge graph used for answering the questions.


The first of these is the Barron's study guide (BARRON'S), consisting of 1200 sentences.

this work instead opts to perform a bidirectional search: when constructing the knowledge base, we add not only the original sentence but also all entailments with subtrees deleted.


We compare these curriculum learning proposals in the context of four non-convex models for QA and show that they lead to real improvements in each of them.

the model considers the quality of word alignment from a hypothesis h ij (formed by combining question-answer candidates (q i , a ij )) to snippets in the textbooks as a proxy for the evidence.


Question answering requires access to a knowledge base to check facts and reason about information.

together with the header, these patterns divide the rows into meaningful columns.


For Lucene, the improvements over all but the Waterloo corpus baseline are statistically significant.

cells that exactly answer the question), 0.5 to cells whose row but not column is aligned in some way to the question (i.e.


Nevertheless, they are easily and abundantly available.

let the set of tables be defined as T = {T 1 , ..., T M }.


Several methods have been used in order to select the summary sentences ranging from binary classifiers (Kupiec et al., 1995), to hidden Markov models (Conroy and O'Leary, 2001), graph-based algorithms (Erkan and Radev, 2004;Mihalcea, 2005), and integer linear programming (Woodsend and Lapata, 2010).

similar neural attention architectures have been previously used for geometry reasoning , under the name Pointer Networks.


Data-driven neural summarization models require a large training corpus of documents with labels indicating which sentences (or words) should be in the summary.


The support of the European Research Council under award number 681760 "Translating Multiple Modalities into Text" is gratefully acknowledged.

we check if a neighbor, represented by pre-trained 4 embeddings, is in the original document and therefore constitutes a valid substitution.


c i = 1 if w i is part of the cue and 0 otherwise.

3 out of 4 examples of suffixal negation in adverbs in the training data (e.g.


Our goal in this work is to create a model that combines the benefits of the existing by-message conditional models with the length-robustness of a by-word conditional method.

the tweets were parsed into word tokens using the twokenizer (Owoputi et al., 2013).


We refer to these as "by-message" methods.

accommodation Theory emphasizes the relatively more communicative and strategic nature of alignment (Giles et al., 1991).


For the normalized entropy measure, the fixed effect of within-episode position is insignificant for initiators, which means there is neither increase nor decrease, and is reliably positive for responders (Switchboard, β = 1.4 × 10 −2 , p < 0.001; BNC, β = 1.2 × 10 −2 , p < 0.001).

it can be seen that entropy drops at the beginning of topic episode, and then increases within the episode.


As the topic develops, the initiators' entropy decreases (Figure 4a) or stays relatively steady (Figure 4b), and the responder's entropy increases.

the mutual information between the initiator's current sentence and the previous context is reduced, which causes the sentence entropy to start high before decreasing.


By processing RelevanceSet = {mQ, mR, mIPD, mHCS, mACB, mICR, mI, mSCD, mPB, mEU, mFP, mED, mHLP, mSI, mSP, mHLC} Having identified the RelevanceSet, we now intend to identify a satisficing combination of the involved motives.

players have domain-specific motives m for participating in the dialogue.


Dialogue acts (DA), such as Statement, Yes-No-Question, Agreement, indicate the sentence pragmatic role as well as the intention of the speakers (Williams, 2012).

information can flow between the two sentences through these states.


Using Freebase (Bol-lacker et al., 2008) or even open information extraction (Fader et al., 2011) would dramatically increase the number of facts and therefore the scope of possible perspectives.

these approaches are limited to contexts in which a relevant perspective already exists.


Although we believe that METEOR and BLEU are reasonable evaluation metrics, they may have not recognize certain paraphrases, in particular paraphrases of entities.

even for many of the entities in SimpleQuestions, the model may perform poorly if the embedding is learned solely based on the SimpleQuestions dataset alone.


these include generating regular expressions (Kushman and Barzilay, 2013), and the code for parsing input documents (Lei et al., 2013).


The same applies to the generation of the attack, health and cost values as each of these predictors is an element in R. Thus, we define our objective function as a marginal log likelihood function over a latent variable ω: Formally, ω is a sequence of pairs r t , s t , where r t ∈ R denotes the predictor that is used at timestamp t and s t the generated string.

yet, it is a promising result that the model was able to capture this difference.


Unfortunately, these approaches define strong assumptions regarding the grammar and structure of the output, which makes it difficult to generalize for other domains (Kwiatkowski et al., 2010).

in the card "Preparation", we observe that while the properties of the card are generated correctly, the effect implements a unrelated one, with the exception of the value 3, which is correctly copied.


experiments performed on our new datasets, and a further pre-existing one, suggest that our extensions outperform strong benchmarks.


if the algorithm generates an identical chunk for the target and one of its distractors, it continues with a less restricted vocabulary and a longer expression.


Generation Procedure In order to generate the above installment triples with our REG system, we simply restrict the vocabulary of the underlying WAC-model.

imageNet (Deng et al., 2009), this model directly captures the relation between actual words used in REs and visual properties of the corresponding referents.


Similarly, we propose to cast REG on images as a word selection task.


whether a dynamic approach to REG compares favourably to the non-dynamic version of our system (see Section 3).

both installment systems have stable performance over time, which indicates that system behaviour is immediately understandable and predictable for human users.


Since the variables are non-negative, one possible choice is an entropy-like regularizer.

we cap each count to five and encode it using five binary indicator features, where the j th feature is set to 1 if the count is j and 0 otherwise.


Here we assume that importance corresponds to entity size.

lEA is available as branch lEA-scorer in the reference implementation of the official CoNll scorer.


in this paper, we report the mention identification effect in the B 3 , CEAF, and BLANC coreference evaluation metrics that makes it impossible to interpret their results properly.


MUC favors the outputs in which entities are over-merged (Luo, 2005).

the more prominent entities of the text get higher importance values.


It computes recall based on the minimum number of missing links in the response entities in comparison to the key entities.

if a mention exists in a response entity, it is considered as a resolved mention regardless of whether it has a correct coreference relation in the response entity.


It is generally recommended to use a stochastic mixture of the reference policy and the current learned policy during rollouts when the reference policy is not optimal (Chang et al., 2015b).

2016extend their mention-ranking model by incorporating entity-level information produced by a recurrent neural network running over the candidate antecedent-cluster.


2016extend their mention-ranking model by incorporating entity-level information produced by a recurrent neural network running over the candidate antecedent-cluster.

(2015) to our model using a deeper neural network, pretrained word embeddings, and more sophisticated pretraining.


This is produced by applying a single fully connected layer of size one to the representation r m (a, m) produced by the mention-pair encoder: where W m is a 1 × d weight matrix.

many recent state-of-the-art coreference systems operate solely by linking pairs of mentions together Martschat and Strube, 2015;Wiseman et al., 2015).


For each example (x, y) ∈ D, the algorithm obtains a trajectory of states x 1 , x 2 , ..., x n visited by the current policy by running it to completion (i.e., repeatedly taking the highest scoring action until reaching an end state) from the start state x.

we instead sort the mentions in descending order by their highest scoring candidate coreference link according to the mention-ranking model.